"""FDR size budget evaluator for NFT-LIM-02 (AZ-441 / AC-7.3).

A 30 min Derkachi replay (4× the 8 min flight) is sampled per-minute
via ``du -sh fdr-output``. The per-minute samples are projected into a
typed ``(monotonic_ms, size_bytes)`` stream by the scenario; this
module extrapolates the 30 min size linearly to 8 h:

    extrapolated_bytes = size_at_30min_bytes / 30 × 480

and asserts ``extrapolated_bytes ≤ 50 GiB`` (AC-2).

AC-1 (the runner actually looped Derkachi for 30 min wall-clock) is
verdict-checked here from the sample timestamps; the scenario test
provides the canonical replay duration as input.

Public-boundary discipline: does NOT import any
``src/gps_denied_onboard`` symbol — inputs are pre-projected typed
samples.
"""

from __future__ import annotations

import csv
from dataclasses import dataclass
from pathlib import Path
from typing import Sequence

GIB_BYTES = 1024**3

REPLAY_WINDOW_MINUTES = 30
EXTRAPOLATION_WINDOW_MINUTES = 8 * 60  # AC-2 — 8 hours
DEFAULT_BUDGET_BYTES = 50 * GIB_BYTES  # AC-2 — ≤ 50 GiB

# AC-1 tolerance: the scenario claims a 30 min replay; in practice the
# wall-clock window may drift by a few seconds due to loop overhead.
# Accept ±60 s slack — anything beyond that is a real replay deviation.
REPLAY_WINDOW_SLACK_MS = 60_000


@dataclass(frozen=True)
class FdrSizeSample:
    """One ``du -sh fdr-output`` sample at a monotonic timestamp."""

    monotonic_ms: int
    size_bytes: int


@dataclass(frozen=True)
class FdrSizeReport:
    """Aggregate NFT-LIM-02 verdict for one run."""

    sample_count: int
    replay_window_ms: int
    size_at_30min_bytes: int | None
    extrapolated_8h_bytes: int | None
    budget_bytes: int
    replay_window_slack_ms: int

    @property
    def passes_replay_window(self) -> bool:
        # AC-1 — actual sampled window is within ±slack of 30 min.
        target_ms = REPLAY_WINDOW_MINUTES * 60_000
        return abs(self.replay_window_ms - target_ms) <= self.replay_window_slack_ms

    @property
    def passes_extrapolation(self) -> bool:
        # AC-2 — extrapolated 8 h size ≤ budget.
        return (
            self.extrapolated_8h_bytes is not None
            and self.extrapolated_8h_bytes <= self.budget_bytes
        )

    @property
    def passes(self) -> bool:
        return self.passes_replay_window and self.passes_extrapolation


def evaluate(
    samples: Sequence[FdrSizeSample],
    *,
    budget_bytes: int = DEFAULT_BUDGET_BYTES,
    replay_window_slack_ms: int = REPLAY_WINDOW_SLACK_MS,
) -> FdrSizeReport:
    """Compute AC-1 + AC-2 verdict from a sorted-or-unsorted sample list."""
    if budget_bytes <= 0:
        raise ValueError(f"budget_bytes must be > 0 (was {budget_bytes!r})")
    if replay_window_slack_ms < 0:
        raise ValueError(
            f"replay_window_slack_ms must be >= 0 (was {replay_window_slack_ms!r})"
        )
    if not samples:
        return FdrSizeReport(
            sample_count=0,
            replay_window_ms=0,
            size_at_30min_bytes=None,
            extrapolated_8h_bytes=None,
            budget_bytes=budget_bytes,
            replay_window_slack_ms=replay_window_slack_ms,
        )
    ordered = sorted(samples, key=lambda s: s.monotonic_ms)
    window_ms = ordered[-1].monotonic_ms - ordered[0].monotonic_ms
    size_at_end = ordered[-1].size_bytes
    extrapolated = int(
        round((size_at_end / REPLAY_WINDOW_MINUTES) * EXTRAPOLATION_WINDOW_MINUTES)
    )
    return FdrSizeReport(
        sample_count=len(ordered),
        replay_window_ms=window_ms,
        size_at_30min_bytes=size_at_end,
        extrapolated_8h_bytes=extrapolated,
        budget_bytes=budget_bytes,
        replay_window_slack_ms=replay_window_slack_ms,
    )


def write_csv_evidence(out_path: Path, report: FdrSizeReport) -> Path:
    """One-row evidence file naming AC-1/AC-2 verdict + sizes."""
    out_path.parent.mkdir(parents=True, exist_ok=True)
    r = report
    with out_path.open("w", newline="") as fh:
        writer = csv.writer(fh)
        writer.writerow(
            [
                "sample_count",
                "replay_window_ms",
                "size_at_30min_bytes",
                "extrapolated_8h_bytes",
                "budget_bytes",
                "replay_window_slack_ms",
                "ac1_replay_window_passes",
                "ac2_extrapolation_passes",
                "passes",
            ]
        )
        writer.writerow(
            [
                r.sample_count,
                r.replay_window_ms,
                "" if r.size_at_30min_bytes is None else r.size_at_30min_bytes,
                "" if r.extrapolated_8h_bytes is None else r.extrapolated_8h_bytes,
                r.budget_bytes,
                r.replay_window_slack_ms,
                "true" if r.passes_replay_window else "false",
                "true" if r.passes_extrapolation else "false",
                "true" if r.passes else "false",
            ]
        )
    return out_path


def write_per_minute_csv(
    out_path: Path, samples: Sequence[FdrSizeSample]
) -> Path:
    """Per-sample CSV (one row per minute) for evidence trend lines."""
    out_path.parent.mkdir(parents=True, exist_ok=True)
    ordered = sorted(samples, key=lambda s: s.monotonic_ms)
    with out_path.open("w", newline="") as fh:
        writer = csv.writer(fh)
        writer.writerow(["index", "monotonic_ms", "size_bytes"])
        for i, s in enumerate(ordered):
            writer.writerow([i, s.monotonic_ms, s.size_bytes])
    return out_path